This notebook accompanies below paper:
F. Sepehrband\*, K.M. Lynch, R.P. Cabeen, C. Gonzalez-Zacarias, L. Zhao, M. D'Arcy, C. Kesselman, M.M. Herting, I.D. Dinov, A.W. Toga, K.A. Clark, **Neuroanatomical Morphometric Characterization of Sex Differences in Youth Using Multivariate Statistical Learning**, *NeuroImage*, submitted September 2017.
In this notebook, we compare results of GLM with SVM. Then, we combine them in a single figure, with the aim of increased interpretability.
author:
Farshid Sepehrband,
Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
farshid.sepehrband@loni.usc.edu
@fsepehrband
import numpy as np
import matplotlib.pyplot as plt
import plotly.plotly as py
import pandas as pd
import os
%pylab inline
Below are maps of statistical measures derived from GLM and SVM. t-statistics of GLM and beta coefficients of SVM are plotted.
from IPython.display import Image
Image(filename='../files/demoFig.png')
Below video compares these maps.
from IPython.display import YouTubeVideo
vid = YouTubeVideo("QjyAV9QPiXU")
display(vid)
Below figure combines SVM beta, GLM t-statistic and correlation with brain size.
Follow the instruction below to work with the interactive plot:

Image(filename='../files/bubble.jpg')